Rational. Mitral valve prolapse (MVP) is a common condition (2-3% in pop.) with frequent complications such as mitral regurgitation (MR), heart failure, ventricular arrhythmia (VA) and even sudden death. Recent data suggest MVP is a more complex pathology, with accumulating evidence of a frequent genuine MVP-Cardiomyopathy (MVP-CM) independent of volume overload, likely associated to the increased risk of sudden death. MVP-CM is thus changing the MVP paradigm. Objective. MUSSET proposes to decipher MVP-CM and to implement a high-level web-tool for diagnosis, prognostic stratification and management guidance of patients with MVP, including AI-imaging analysis. MUSSET aims to deeply and fully characterize MVP/MVP-CM and related complications for ensuring 100% proper diagnostic and offering all support elements for guiding MVP-CM management and its arrhythmia consequences to improve patient outcome. MUSSET's final goal is to offer an individualized, simplified management helping master the complexity implemented in a MVP vertical web-tool integrating personalized clinical, imaging data, biomarkers, comprehensive support and risk score. Methods. MUSSET is an entirely original project capitalizing on already constituted large clinical cohort of patients, with all MVP phenotypes and the availability of imaging, genetics, biologic, and bioinformatics capabilities within the consortium. MUSSET is built around 5 dimensions implemented in synergetic work packages: 1.The end goal is the development with industrial partners of a MVP/MVP-CM-personalized web tool. It will be set up in several phases of knowledge integration, as follows: i) Integration of clinical data of retrospective cohorts and diagnostic criteria identification, ii) Construction of the diagnostic algorithm, including imaging post processes with AI analysis, iii) Implementation of ECG and VA data, iv) Integration of prospective cohort data and patients follow-up data, v) Implementation of prognostic algorithm, vi) Tool validation with prospective cohort of replication, vii) Implementation of biomarkers/genetic data. The scientific knowledge incorporated will include WP 2 to 5 : 2.Comprehensive characterization of MVP/MVP-CM to determine the diagnostic criteria of MVP-CM, by algorithm or machine-learning in complex cases. MVP characteristics and management in cardiology practice, and its sex/gender expression, will be assessed. Personalized and predictive care based on real observed outcome in large cohorts will be captured under medical treatment and after surgery. 3.VA, a frequent and worrying manifestation of MVP-CM, will be fully characterized. The consortium will rely on classical rest and exercise ECG characteristics in MVP-CM, and on mid-term recording and multiple electrodes tool to decipher VA for patients at higher risk. Identification of patients at risk of life-threatening VA and VA management in these patients is an essential goal of the proposal. 4.Myocardial mechanics and remodeling including replacement fibrosis, a key marker of MVP-CM and VA risk, will be explored by advanced imaging analyses. Left ventricular (LV) and myocardial remodeling of MVP-CM will be assessed based on 2 successive CMR examinations performed 3-years apart (450 CMR performed yet). Development of new CMR sequences and dedicated software will help reach this target. Development of semi-automatized CMR and echocardiography images analyses with AI-based tools will enable a simplified evaluation of mitral valve apparatus and cardiac chambers alteration. 5.Frequent and rare genetic variants involved in isolated or familial forms of MVP-CM, and their relation to VA, will be unraveled. Myocardial tissue and mitral leaflet tissue will be analyzed for transcriptome expression. Zebrafish model will screen the effect of the best candidate genes that would affect both mitral valve and myocardium structure and function. A rodent model of MVP-CM will allow to decipher myocardial alteration.